Abstract

Fault Detection and Isolation (FDI) using Linear Kalman Filter (LKF) is not sufficient for effective monitoring of nonlinear processes. Most of the chemical plants are nonlinear in nature while operating the plant in a wide range of process variables. In this study we present an approach for designing of Multi Model Adaptive Linear Kalman Filter (MMALKF) for Fault Detection and Isolation (FDI) of a nonlinear system. The uses a bank of adaptive Kalman filter, with each model based on different fault hypothesis. In this study the effectiveness of the MMALKF has been demonstrated on a spherical tank system. The proposed method is detecting and isolating the sensor and actuator soft faults which occur sequentially or simultaneously.

Keywords:

Fault detection and isolation, multi model adaptive linear kalman filter, nonlinear, residual generation, spherical tank, state estimation,

References

1. Anjali, P.D. and S.C. Patwardhan, 2008. Online fault diagnosis in nonlinear systems using the multiple operating regime approach. Ind. Eng. Chem. Res., 47(17): 6711-6726.
   CrossRef    
2. Basseville, M. and I.V. Nikiforov, 1993. Detection of abrupt changes theory and applications. Prentice-Hall, Englewood Cliffs,N.J., USA.
   
3. Danielle, D. and D. Cooper, 2003. A practical multiple model adaptive strategy for multivariable model predictive control. Control Eng. Pract., 11: 649-664.
   CrossRef    
4. Frank, P.M., 1990. Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results. Automatica, 26(3): 459-474.
   CrossRef    
5. Gertler, J.J., 1998. Fault Detection and Diagnosis in Engineering Systems. Marcel Dekker, New York.
   
6. Isermann, R., 1984. Process fault detection based on modeling and estimation methods: A survey. Automatica, 20: 387-404.
   CrossRef    
7. Manimozhi, M., G. Shigdha, Nagalakshmi, R. Saravana Kumar, 2013, State estimation and sensor bias detection using adaptive linear Kalman filter. Int. Rev. Modell. Simul., 6(3): 1005-1010.
   
8. Patton, R.J. and J. Chen, 1997. Observer-based fault detection and isolation: Robustness and applications. Control Eng. Pract., 5(5): 671-682.
   CrossRef    
9. Prakash, J., S.C. Patwardhan and S. Narasimhan, 2002. A supervisory approach to fault tolerant control of linear multivariable systems. Ind. Eng. Chem. Res., 41: 2270-2281.
   CrossRef    
10. Vinodha, R., S.L. Abraham and J. Prakash, 2010. Multiple model and neural based adaptive multi-loop PID controller for a CSTR process. World Acad. Sci. Eng. Technol., 68: 505-510.
    
11. Willsky, A.S., 1976. A survey of design methods for failure detection in dynamic systems. Automatica, 12: 601-611.
    CrossRef    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.